Compara mètodes

	Classificació d'actes de diàleg	Detecció d'intencions	Ompliment de ranures	Classificació de text
Camp	Mineria de text	Mineria de text	Mineria de text	Mineria de text
Família	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	1997–2000	—	2018 (joint slot-gate model); BIO tagging foundations earlier	—
Autor original	Stolcke et al.; Jurafsky et al.	—	Established via NER/IOB tagging literature; popularised for dialogue by Goo et al. (2018) and Chen et al. (2019)	—
Tipus	NLP utterance-classification task	NLP / NLU text-classification task	NLP token-classification / information-extraction task	Supervised NLP classification task
Font seminal	Stolcke, A. et al. (2000). Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics, 26(3), 339-373.	Larson, S. et al. (2019). An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction. EMNLP.	Goo, C.W., Gao, G., Hsu, Y.K., Huo, C.L., Chen, T.C., Hsu, S.C., & Chen, Y.N. (2018). Slot-Gated Modeling for Joint Slot Filling and Intent Prediction. Proceedings of NAACL-HLT 2018.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Àlies	dialogue act tagging, speech act classification, Diyalog Eylem Sınıflandırma (Dialogue Act Classification)	intent classification, intent recognition, Niyet Tespiti (Intent Detection)	slot doldurma, Slot Doldurma (Slot Filling / NER-NLU), information slot extraction, dialogue slot filling	text categorization, document classification, topic classification, metin sınıflandırma
Relacionats	4	4	5	4
Resum	Dialogue act classification is a natural-language-processing task that automatically labels the communicative function of each utterance in a conversation — such as question, answer, greeting, or rejection. Consolidated by Jurafsky et al. (1997) and Stolcke et al. (2000), it is a foundational component for chatbots and discourse analysis.	Intent detection is a natural-language-understanding task that classifies the purpose behind a user utterance — such as making a reservation, asking for information, or filing a complaint — into one of a set of predefined intent classes. It is a core NLU component of conversational interfaces and customer-service automation systems, drawing on the benchmarks of Larson et al. (2019) and Casanueva et al. (2020).	Slot filling is a natural-language-understanding task that extracts predefined template fields — such as date, location, or product name — from a user utterance. It emerged as a core component of dialogue systems and form-based information extraction, and became widely studied after Goo et al. (2018) introduced the Slot-Gated Model for joint slot filling and intent prediction, followed by Chen et al. (2019) who extended the paradigm with BERT-based joint modelling.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.